- 600B total / 27B active MoE, with 1M context + Vision
- Substantially lower task cost at comparable intelligence
- Broad software engineering capabilities with sustained execution over long horizons
- 600B total / 27B active MoE, with 1M context + Vision
- Substantially lower task cost at comparable intelligence
- Broad software engineering capabilities with sustained execution over long horizons
huggingface.co/stepfun-ai/S...
huggingface.co/stepfun-ai/S...
✨ Base & Base-Midtrain
✨ 196B total/11B active - Apache 2.0
✨ 256K context
✨ High-speed reasoning & agentic tasks
huggingface.co/stepfun-ai/S...
huggingface.co/stepfun-ai/S...
✨ Base & Base-Midtrain
✨ 196B total/11B active - Apache 2.0
✨ 256K context
✨ High-speed reasoning & agentic tasks
A sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token.
Blog: static.stepfun.com/blog/step-3....
Model: huggingface.co/stepfun-ai/S...
A sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token.
Blog: static.stepfun.com/blog/step-3....
Model: huggingface.co/stepfun-ai/S...
*Trained SOTA small models.
*Published open datasets reused by Nvidia, Anthropic, Stepfun, Jina… and maybe more importantly, many independent researchers.
*Proved full synthetic training is viable in small size range (I have little doubts it will scale).
*Shipped a few blogposts
*Trained SOTA small models.
*Published open datasets reused by Nvidia, Anthropic, Stepfun, Jina… and maybe more importantly, many independent researchers.
*Proved full synthetic training is viable in small size range (I have little doubts it will scale).
*Shipped a few blogposts
It runs in your terminal and handles the full task loop—reading code, making changes, and running tests. It works with the Step provider and discovers the available Step models after sign-in. MCP servers, Agent Skills, plugins, and multi-agent orchestration
github.com/stepfun-ai/S...
It runs in your terminal and handles the full task loop—reading code, making changes, and running tests. It works with the Step provider and discovers the available Step models after sign-in. MCP servers, Agent Skills, plugins, and multi-agent orchestration
github.com/stepfun-ai/S...
Nice!
Dataset: huggingface.co/datasets/ste...
Project: static.stepfun.com/blog/step-3....
Nice!
Dataset: huggingface.co/datasets/ste...
Project: static.stepfun.com/blog/step-3....
A SOTA text-to-video pre-trained model with 30B parameters, capable of generating videos up to 204 frames long.
Models:
huggingface.co/stepfun-ai/s...
and
huggingface.co/stepfun-ai/s...
A SOTA text-to-video pre-trained model with 30B parameters, capable of generating videos up to 204 frames long.
Models:
huggingface.co/stepfun-ai/s...
and
huggingface.co/stepfun-ai/s...
huggingface.co/collections/...
huggingface.co/collections/...
- Test-Time Compute Scaling
- Deep Audio Comprehension
- Real-time responsiveness
- Scalable chain-of-thought reasoning for audio tasks
Comparable to Gemini 3 across major audio reasoning tasks.
huggingface.co/stepfun-ai/S...
- Test-Time Compute Scaling
- Deep Audio Comprehension
- Real-time responsiveness
- Scalable chain-of-thought reasoning for audio tasks
Comparable to Gemini 3 across major audio reasoning tasks.
huggingface.co/stepfun-ai/S...
Startups like Moonshot, Zhipu, MiniMax, StepFun, 01 ai and Baichuan are focusing on improving compute efficiency
tinyurl.com/5exjeb97
@bloomberg.com
#AI
Startups like Moonshot, Zhipu, MiniMax, StepFun, 01 ai and Baichuan are focusing on improving compute efficiency
tinyurl.com/5exjeb97
@bloomberg.com
#AI
- Free alternative to expensive GPT Realtime API
- End-to-end speech I/O
- Advanced speech & audio understanding
- Expressive prosody control
- Intelligent speech conversation
- Web search support
- Free alternative to expensive GPT Realtime API
- End-to-end speech I/O
- Advanced speech & audio understanding
- Expressive prosody control
- Intelligent speech conversation
- Web search support
HuggingFace: huggingface.co/stepfun-ai/S...
GGUF: huggingface.co/stepfun-ai/S...
ModelScope: modelscope.cn/models/stepf...
API: platform.stepfun.ai
Blog: static.stepfun.com/blog/step-3....
HuggingFace: huggingface.co/stepfun-ai/S...
GGUF: huggingface.co/stepfun-ai/S...
ModelScope: modelscope.cn/models/stepf...
API: platform.stepfun.ai
Blog: static.stepfun.com/blog/step-3....
firethering.com/stepfun-step...
#stepfun #claude #opus #coding #ai #technews #tech #llm #opensource
firethering.com/stepfun-step...
#stepfun #claude #opus #coding #ai #technews #tech #llm #opensource
400 TPS. 198B sparse MoE, ~11B active. 256K context, 3 reasoning levels, built for agentic, coding, search, and multimodal workflows — balancing speed, cost, and reliable execution.
400 TPS. 198B sparse MoE, ~11B active. 256K context, 3 reasoning levels, built for agentic, coding, search, and multimodal workflows — balancing speed, cost, and reliable execution.
(Here are some recent pictures of Eddie, née ShortJorts, from my Uncle Stefan.)
(Here are some recent pictures of Eddie, née ShortJorts, from my Uncle Stefan.)
huggingface.co/stepfun-ai/S...
✨ Apache 2.0
✨ Combines dual-brain architecture and acoustic-grounded reasoning to enable real-time dialogue with SOTA-level reasoning
huggingface.co/stepfun-ai/S...
✨ Apache 2.0
✨ Combines dual-brain architecture and acoustic-grounded reasoning to enable real-time dialogue with SOTA-level reasoning